A Temporal Graph Network Approach for Personalized Portfolio Recommendations

Ziyu Feng ()
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Ziyu Feng: Wuhan No. 39 Middle School

A chapter in Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), 2024, pp 520-526 from Springer

Abstract: Abstract In volatile financial markets, individual investors face challenges as traditional recommendation systems focus on individual stocks and rely mainly on historical data, overlooking social media sentiment, news, and expert opinions. This paper presents a framework using temporal graph networks (TGN) to capture evolving stock dynamics and investor preferences. By integrating user preferences-such as risk tolerance and investment goals-with alternative data, the model offers personalized portfolio recommendations. Evaluated on a large dataset of stock prices, transactions, and alternative data, the framework outperforms traditional methods in risk-adjusted returns, diversification, and alignment with investor goals.

Keywords: Stock recommendation; portfolio management; temporal graph networks; personalized financial advice; recommender systems (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-598-0_54

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DOI: 10.2991/978-94-6463-598-0_54

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